Risk adjustment coding is complex and time-consuming. Staffing shortages and a reliance on legacy systems make it difficult to keep up with the growing amount of structured and unstructured data. MA organizations and healthcare payers need to strike a balance between accuracy, performance and productivity. A modern AI-Driven solution is helping them do it:
AI-Powered HCC Training
HCC coding is getting harder, and accuracy is more important than ever. Increasingly, payers and providers are subject to RADV and compliance audits that demand that every submitted HCC code is supported by appropriate documentation. With that in mind, it’s crucial to have a scalable solution to help your medical coders deliver coding results that can withstand these rigorous compliance reviews while improving productivity and accuracy.
The amount of patient data collected today is exponentially growing, and healthcare organizations need more resources to review it. As a result, they rely on large coding teams and data analytics staff to scan and analyze the massive volume of structured and unstructured data. AI-based tools can free up healthcare staff to focus on revenue-boosting activities. A SaaS-based HCC risk adjustment coding training platform can automate identifying missed coding opportunities and unsupported known codes, saving medical coders time by eliminating repetitive tasks and delivering a more accurate coding result.
AI-Powered HCC Review
Hepatocellular carcinoma (HCC) is a leading cause of death worldwide due to late diagnosis. Despite recent breakthroughs in treatment, it remains challenging for physicians to identify HCC and accurately determine its prognosis. AI can make the characterization, staging and management of patients with HCC easier by automating the review process. This allows for faster, more accurate risk adjustment coding and helps improve revenue performance. The use of AI in detecting and diagnosing HCC has been rapidly advancing in recent years. However, clinical adoption of this technology could be faster. Robust validation, large-scale studies, multicentre cooperation, AI advocacy, and technology education among clinicians are needed to advance this field. To address this, the ML platform uses advanced natural language processing (NLP) to simplify and speed up the risk adjustment coding workflow. It automatically reviews medical records for Medicare Advantage (MA) plan members and identifies net-new encounters, diagnoses, HCCs and related codes. This reduces manual work for coders and improves productivity by up to 45%. The NLP engine can also be trained with custom-defined rules to deliver a more accurate and consistent HCC discovery rate.
AI-Powered HCC QA
HCC coding accuracy is essential for healthcare organizations to maximize revenue, meet regulatory compliance and improve patient care. However, staffing challenges plague many independent clinicians and physician groups due to a need for well-trained HCC risk adjustment coding professionals. The key to a successful RAF score submission is accurate ICD-10-CM coding and documentation. Insurance companies use this data to assign a member’s RAF score and predict costs. When errors occur, they can significantly impact an organization’s bottom line and disrupt patient care. Traditional QA processes require medical coders to manually comb through thousands of charts, looking for documented chronic conditions. This time-consuming and resource-intensive process often results in missed RAF scoring opportunities and under-documentation.
The good news is that an AI-powered QA solution can save healthcare organizations time and money while improving coding accuracy. The best-in-class solutions leverage Precise Word Matching AI to identify over 96% of RAF-eligible codes before the coder even starts reviewing medical records. These solutions also automate second-level reviews and eliminate costly coding errors with high accuracy, precision and consistency.
AI-Powered HCC Reporting
As CMS and the Office of Inspector General (OIG) continue scrutinizing HCC risk adjustment activities, MA organizations and healthcare payers seek technology supporting their efforts. Getting the job done with tighter coding accuracy standards and a comprehensive review process is challenging and time-consuming for medical coders and staff. AI can significantly accelerate the RA workflow and reduce risk adjustment expenses by improving productivity and ensuring compliance. It can help identify missing supported HCCs and identify coded HCCs lacking sufficient evidence, delivering the confidence that an RAF score submission will be compliant.
An AI-enabled RA platform can also prioritize medical records and provide actionable information to medical coders and QA teams about which diagnoses will most likely support an HCC. This can be a critical factor in minimizing the likelihood of an audit while ensuring accurate medical coding for risk adjustment purposes. An end-to-end RA solution powered by AI can automatically collect, collate, review, and submit the required data to meet CMS submission requirements. An AI-driven evidence validation engine uses natural language processing, optical character recognition, and machine learning to automate chart verification, validate administrative claims data, and identify net-new encounters, diagnoses, and hierarchical condition categories. Broad match technology and confidence scoring help eliminate false positives and deliver first-pass ideal HCC discovery accuracy of 98%, driving up to 45% improvement in productivity.
Streamlined workflows
Hierarchical condition category (HCC) coding assigns medical diagnoses to categories based on severity and expected healthcare costs. Those categories are then used to calculate a risk adjustment factor (RAF) score, which is applied to the member’s capitation rate. Inaccurate RAF scores can lead to costly reimbursement denials for a health plan. This is particularly true for Medicare Advantage organizations, which must accurately capture a member’s complete diagnosis profile to maximize risk adjustment performance and reimbursement. Using an AI-powered risk adjustment coder and QA tool, health plans can streamline their HCC coding processes to ensure that all RAF scoring opportunities are captured. This includes identifying missed conditions and addressing gaps in documentation. AI-powered tools can also help with prospective review by identifying suspect diagnoses and chronic condition management opportunities for physicians before the patient encounter. This capability reduces physician burnout and eliminates the need for expensive, time-consuming retrospective chart reviews.
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